AIMC Topic: Machine Learning

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Subjective machines: Probabilistic risk assessment based on deep learning of soft information.

Risk analysis : an official publication of the Society for Risk Analysis
For several years machine learning methods have been proposed for risk classification. While machine learning methods have also been used for failure diagnosis and condition monitoring, to the best of our knowledge, these methods have not been used f...

Development and External Validation of a Machine Learning Model for Prediction of Potential Transfer to the PICU.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
OBJECTIVES: Unrecognized clinical deterioration during illness requiring hospitalization is associated with high risk of mortality and long-term morbidity among children. Our objective was to develop and externally validate machine learning algorithm...

Automated Machine Learning Pipeline Framework for Classification of Pediatric Functional Nausea Using High-Resolution Electrogastrogram.

IEEE transactions on bio-medical engineering
OBJECTIVE: Pediatric functional nausea is challenging for patients to manage and for clinicians to treat since it lacks objective diagnosis and assessment. A data-driven non-invasive diagnostic screening tool that distinguishes the electro-pathophysi...

Weakly Supervised Occupancy Prediction Using Training Data Collected via Interactive Learning.

Sensors (Basel, Switzerland)
Accurate and timely occupancy prediction has the potential to improve the efficiency of energy management systems in smart buildings. Occupancy prediction heavily depends on historical occupancy-related data collected from various sensor sources. Unf...

Machine Learning for Touch Localization on an Ultrasonic Lamb Wave Touchscreen.

Sensors (Basel, Switzerland)
Classification and regression employing a simple Deep Neural Network (DNN) are investigated to perform touch localization on a tactile surface using ultrasonic guided waves. A robotic finger first simulates the touch action and captures the data to t...

Machine Learning Prediction of Clinical Trial Operational Efficiency.

The AAPS journal
Clinical trials are the gatekeepers and bottlenecks of progress in medicine. In recent years, they have become increasingly complex and expensive, driven by a growing number of stakeholders requiring more endpoints, more diverse patient populations, ...

GEOM, energy-annotated molecular conformations for property prediction and molecular generation.

Scientific data
Machine learning (ML) outperforms traditional approaches in many molecular design tasks. ML models usually predict molecular properties from a 2D chemical graph or a single 3D structure, but neither of these representations accounts for the ensemble ...

Artificial intelligence in gastrointestinal and hepatic imaging: past, present and future scopes.

Clinical imaging
The use of technology in medicine has grown exponentially because of the technological advancements allowing the digitization of medical data and optimization of their processing to extract multiple features of significant clinical relevance. Radiolo...

Assigning diagnosis codes using medication history.

Artificial intelligence in medicine
Diagnosis assignment is the process of assigning disease codes to patients. Automatic diagnosis assignment has the potential to validate code assignments, correct erroneous codes, and register completion. Previous methods build on text-based techniqu...

Machine Learning for Cardiovascular Biomechanics Modeling: Challenges and Beyond.

Annals of biomedical engineering
Recent progress in machine learning (ML), together with advanced computational power, have provided new research opportunities in cardiovascular modeling. While classifying patient outcomes and medical image segmentation with ML have already shown si...